The health consequences of obesity history and weight fluctuations in adulthood
Bibliographic record
Abstract
Background: The prevalence of Canadians living with obesity has increased over the past four decades. Disease and mortality risk increase as the number of years lived with obesity increases. Methods: This study used self-reported weight history and health data collected from 2007 to 2011 via the Canadian Health Measures Survey (n = 5,761) to examine whether increased exposure to obesity during adulthood increases the odds of having poor health outcomes. Results: The percentage of respondents with an obesity-related chronic condition was lower among those who did not have obesity at the time of survey or report having obesity in the past (50.6%) compared with those who did not have obesity at the time of the survey but did in the past (65.9%) or who had obesity at the time of the survey and in the past (71.1%). Relative to never having obesity, having obesity in the past but not at present or having obesity in the past and at present were associated with increased odds of having a range of chronic conditions. The highest odds were observed for type 2 diabetes (odd ratio (OR) = 3.26, 95% confidence interval (CI): 2.40 to 4.43 and OR = 5.36, 95% CI: 3.88 to 7.41), hypertension (OR = 2.41, 95% CI: 1.69 to 3.44 and OR = 3.76, 95% CI: 2.84 to 4.97), and poor or fair self-rated general health (OR = 2.04, 95% CI: 1.51 to 2.76 and OR = 2.68, 95% CI: 2.11 to 3.40). Interpretation: Having had obesity in the past, regardless of current obesity status, was associated with increased odds of poor health outcomes. Obesity history information should be considered when estimating the population burden of obesity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".